Signal filtering is crucial to yielding high quality signals, especially in medical signal acquisition where signals are in the millivolt level, such as surface electrocardiogram (ECG), intra-cardiac electrograms (ICEG) signals and invasive blood pressure signals. In clinical applications, frequency bandwidth controlled filters (such as high pass, low pass, band pass filters) are known to improve signal to noise ratio and achieve good quality patient signals for clinical diagnosis and patient status characterization. However different portions of patient signals (such as ECG, ICEG, blood pressure, SPO2 (blood oxygen saturation) and respiration signals) usually have different characteristics and different signal frequency components. However, known signal processing and filtering methods typically fail to efficiently remove unwanted noise and filter out true patient response signals since, 1. the frequency structure and signal-noise sub-components are time varying, 2. the signal and noise are dependent on characteristics of the different signal portions, such as a QRS complex and P wave in an ECG signal because these signals are created from different functional tissue and chambers and 3. the patient signal creation, patient function procedure and patient noise components are usually nonlinear and known filters are typically linear and fail to reliably and adaptively filter the noise.
Signal acquisition units need to process signals having multiple types of noise that are variable in amplitude, frequency, function and pattern, such as patient movement noise, power line electrical noise, electrical and magnetic noise from other medical instruments in hospitals, in order to resolve a clean signal from an input source. Known filtering systems have limited ability to reduce color noise and artifact interference which shares a frequency band with cardiac signals (overlap). Known filter systems are typically linear using fixed coefficients in a digital filter and lack adaptive filtering capability desirable for use in accommodating signal transitions such as a normal to arrhythmia transition, ventricular tachycardia (VT), ventricular fibrillation (VF), myocardial ischemia or infarction. Known linear signal processing systems fail to effectively reduce nonlinear and non-stationary noise and artifacts, in cardiac signals.
The noise concerned comprises patient biological noise due to respiration or patient movement, for example and due to procedures (pacing, ablation, defibrillation, electrical cutting). In addition a noise cut off frequency may shift due to treatment. Known patient signal filters are unable to efficiently remove variable noise from patient signals. Further, known fixed low or high frequency band pass filtering fails to effectively track and cancel dynamic noise and artifacts (especially, broad band noise and semi-white noise), such as voltage/current leakage noise from an electro-cautery instrument and cardiac ablation unit. A system according to invention principles addresses these deficiencies and elated problems.